Moderating effect of COVID-19 on e-learning predictors: an empirical study on student's perspective in Sindh, Pakistan

• Main Authors: Keerio, Imran Khan, Shah, Asadullah, Luhana, Krishan Kumar, Muhamad Ibrahim, Najhan, Mohd Mohadis, Hazwani
• Format: Article
• Language: English
• Published: Cosmos Scholars Publishing House 2023
• Subjects: T10.5 Communication of technical information
• Online Access: http://irep.iium.edu.my/107228/1/107228_Moderating%20effect%20of%20COVID-19.pdf; http://irep.iium.edu.my/107228/; https://www.cosmosscholars.com/phms/index.php/ijmst/article/view/2090

During the COVID-19 pandemic, e-learning has been crucial for maintaining educational continuity while adhering to safety protocols and using social distancing guidelines. The worldwide spread of the pandemic has significantly disrupted traditional face-to-face educational practices, compelling the rapid and extensive adoption of electronic learning alternatives. As a result, it is crucial to understand the key factors influencing e-learning adoption in developing countries such as Pakistan during COVID-19. This research aims to analyze the moderating role of COVID19 on key e-learning predictors using the UTAUT model by including some external Constructs. This study employed a qualitative survey-based methodology, with a sample size of 460 responses from students who engaged in e-learning during the period of the COVID-19 pandemic. The PLS-SEM technique was employed for data analysis utilizing the SmartPls4 version 4.0.9.2 program. A Model was formed based on the UTAUT model by including some external variables. Empirical results revealed that during COVID-19 the most important factors predicting student behavior intention for e-learning adoption were, Computer anxiety, System characteristics, Facilitating conditions, Social influence, and COVID-19 itself. Furthermore, results also demonstrate that there was a sizable amount of COVID-19 moderating effect (0.022) on the association between social Influence and Behavior intention which somehow weakened the adoption of e-learning during COVID-19 time. The R-square value of the model is (0.677), which indicates that the model possesses substantial explanatory power, and the Q-square value is (0.662) indicating the establishment of Model predictive relevance.